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- W2072326387 abstract "It has been previously observed that optimization of the [email protected] relevance objective (i.e., a set-based objective that is 1 if at least one document is relevant, otherwise 0) empirically correlates with diverse retrieval. In this paper, we proceed one step further and show theoretically that greedily optimizing expected [email protected] w.r.t. a latent subtopic model of binary relevance leads to a diverse retrieval algorithm sharing many features of existing diversification approaches. This new result is complementary to a variety of diverse retrieval algorithms derived from alternate rank-based relevance criteria such as average precision and reciprocal rank. As such, the derivation presented here for expected [email protected] provides a novel theoretical perspective on the emergence of diversity via a latent subtopic model of relevance --- an idea underlying both ambiguous and faceted subtopic retrieval that have been used to motivate diverse retrieval." @default.
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- W2072326387 date "2011-10-24" @default.
- W2072326387 modified "2023-09-23" @default.
- W2072326387 title "Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model" @default.
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- W2072326387 doi "https://doi.org/10.1145/2063576.2063869" @default.
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